Computational Scientist

What You Will Do

• Develop and apply hybrid models that combine first-principles understanding (chemistry, materials behavior) with data-driven methods to accelerate product development.
• Analyze experimental and field data to uncover relationships between formulation, processing conditions, and product performance.
• Build predictive models to support:
  • Formulation optimization
  • Performance prediction
  • Product stability and robustness
  • Quality improvement
• Design and support experiments (DOE) to efficiently generate data for model development and validation.
• Collaborate closely with R&D scientists, chemists, and product developers to guide formulation strategies and decision-making.
• Develop data-driven insights and digital tools to reduce development cycle time and improve success rates.
• Ensure model validity through proper validation, uncertainty quantification, and alignment with domain knowledge.
• Document methodologies, assumptions, and modeling workflows to support reproducibility and knowledge sharing.
• Contribute to development of reusable modeling frameworks and best practices for data-driven product innovation.

About You

• Strong foundation in hybrid modeling, integrating domain knowledge (chemistry, formulations, materials behavior) with statistical and machine learning approaches.
• Experience applying modeling to product development, formulation optimization, and performance prediction.
• Solid understanding of experimental data (lab, pilot, and field) and how to incorporate it into predictive models.
• Proficiency in Python or other programing languagesfor data analysis, modeling, and automation.
• Experience with time-series and experimental datasets related to product performance, stability, and quality.
• Demonstrated ability to translate complex modeling results into insights that guide product design and development decisions.
• Strong communication skills and ability to collaborate with scientists, chemists, and product developers.
• Familiarity with SQL and modern data platforms (Databricks, Azure, AWS, Snowflake) is a plus.
• Ability to work independently and within cross-functional R&D teams.

Skills/Qualifications

• PhD or Master's degree in Chemical Engineering, Materials Science, Mechanical Engineering, Data Science, or a related field.
• For Master's candidates, minimum 3+ years of industry experience required experience in downstream, chemical, or materials/product development environments is strongly preferred.
• Strong preference for candidates with experience in:
• Formulation modeling or property prediction
• Hybrid modeling applied to product or material performance

Your Benefits

EEO Statement

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